Hasil untuk "Men"

W. Kannel, W. Castelli, T. Gordon et al.

Susan Nolen-Hoeksema

S. Basaria, A. Coviello, T. Travison et al.

D. Gottlieb, G. Yenokyan, A. Newman et al.

Frank Edwards, Hedwig Lee, Michael H. Esposito

Significance Police violence is a leading cause of death for young men in the United States. Over the life course, about 1 in every 1,000 black men can expect to be killed by police. Risk of being killed by police peaks between the ages of 20 y and 35 y for men and women and for all racial and ethnic groups. Black women and men and American Indian and Alaska Native women and men are significantly more likely than white women and men to be killed by police. Latino men are also more likely to be killed by police than are white men. We use data on police-involved deaths to estimate how the risk of being killed by police use of force in the United States varies across social groups. We estimate the lifetime and age-specific risks of being killed by police by race and sex. We also provide estimates of the proportion of all deaths accounted for by police use of force. We find that African American men and women, American Indian/Alaska Native men and women, and Latino men face higher lifetime risk of being killed by police than do their white peers. We find that Latina women and Asian/Pacific Islander men and women face lower risk of being killed by police than do their white peers. Risk is highest for black men, who (at current levels of risk) face about a 1 in 1,000 chance of being killed by police over the life course. The average lifetime odds of being killed by police are about 1 in 2,000 for men and about 1 in 33,000 for women. Risk peaks between the ages of 20 y and 35 y for all groups. For young men of color, police use of force is among the leading causes of death.

J. Bayart

A. Jemal, R. Tiwari, Taylor Murray et al.

L. Chambless, G. Heiss, A. Folsom et al.

R. Paffenbarger, R. T. Hyde, A. Wing et al.

W. Catalona, Deborah S. Smith, T. Ratliff et al.

N. Sattar, A. Gaw, O. Scherbakova et al.

W. Kannel, R. Abbott, D. Savage et al.

V. Roger, S. Weston, M. Redfield et al.

João Brázia, István Z. Kiss, Alexandre P. Francisco et al.

Men who have sex with men (MSM) remain disproportionately affected by HIV, yet optimizing Pre-exposure Prophylaxis (PrEP) distribution remains a public health challenge. Current guidelines and most modelling studies do not incorporate sociodemographic or network-level factors that shape transmission. While network reconstruction from egocentric data has been studied, the relative importance of demographic mixing dimensions remains uncertain. Using data from 4,667 MSM participants, we show that uncertainty in network reconstruction from egocentric survey data - specifically whether assortativity by age or race is incorporated - affects simulated HIV prevalence under the same observed PrEP uptake. We simulate HIV transmission over 50 years across this structural space and evaluate whether empirically observed uptake reaches transmission-critical network positions. Network structure strongly influences outcomes: assortative by degree networks show 17% lower equilibrium prevalence due to hub isolation within communities. Targeted PrEP strategies based on degree or k-shell centrality achieved the highest prevalence reductions, particularly in assortative by age and race networks where hubs bridge demographic groups. PrEP uptake from data is suboptimal in assortative by age and race networks, underperforming compared with network-based strategies. Results demonstrate that uncertainty in network reconstruction affects intervention design and highlight the need for robust prevention strategies under structural ambiguity.

Mohammad S. Parsa, Lukasz Golab

Migraine literacy among the public is known to be low, and this lack of understanding has a negative impact on migraineurs' quality of life. To understand this impact, we use text mining methods to study migraine discussion on the Reddit social media platform. We summarize the findings in the form of "four things people should know about chronic migraines": it is a serious disease that affects people of all ages, it can be triggered by many different factors, it affects women more than men, and it can get worse in combination with the COVID-19 virus.

Sonal Prabhune, Balaji Padmanabhan, Kaushik Dutta

We investigate the existence and persistence of a specific type of gender bias in some of the popular LLMs and contribute a new benchmark dataset, RealWorldQuestioning (released on HuggingFace ), developed from real-world questions across four key domains in business and health contexts: education, jobs, personal financial management, and general health. We define and study entropy bias, which we define as a discrepancy in the amount of information generated by an LLM in response to real questions users have asked. We tested this using four different LLMs and evaluated the generated responses both qualitatively and quantitatively by using ChatGPT-4o (as "LLM-as-judge"). Our analyses (metric-based comparisons and "LLM-as-judge" evaluation) suggest that there is no significant bias in LLM responses for men and women at a category level. However, at a finer granularity (the individual question level), there are substantial differences in LLM responses for men and women in the majority of cases, which "cancel" each other out often due to some responses being better for males and vice versa. This is still a concern since typical users of these tools often ask a specific question (only) as opposed to several varied ones in each of these common yet important areas of life. We suggest a simple debiasing approach that iteratively merges the responses for the two genders to produce a final result. Our approach demonstrates that a simple, prompt-based debiasing strategy can effectively debias LLM outputs, thus producing responses with higher information content than both gendered variants in 78% of the cases, and consistently achieving a balanced integration in the remaining cases.

Ryusuké Ebiné

In his art criticism of the 1840s, Baudelaire sought, by emphasising the importance of colour over drawing, a parallel between the artistic unity created by a superior artist and the collective unity of artists who gather around a master. However, from the 1850s onwards, he was not always seeking unity in art as a symbol of collective order among men, because, in his view, there were no longer any artists capable of leading mediocre individuals. Nevertheless, Baudelaire also understood that freedom and equality lead to universal violence. This explains, on the one hand, his pessimistic view of the world, based on the idea of original sin, and, on the other hand, the dynamic nature of his poetic works, in which a stable ideal state is never established.

Rubén Linares-Navarro, Iván Sanz-Muñoz, Iván Sanz-Muñoz et al.

BackgroundThe recent Monkeypox (Mpox) outbreak has disproportionately affected men who have sex with men (MSM), amplifying stigma and discrimination. While prior research examined media portrayals and public perceptions, little is known about MSM’s direct experiences. To address this gap, we assess discrimination, stigma, and psychosocial impact across social and healthcare settings.MethodsA cross-sectional observational study was conducted using a structured, pilot-tested survey to assess discrimination against MSM in media, family, socio-occupational, and healthcare environments.ResultsAmong 115 MSM surveyed, 81.7% observed discriminatory comments in media, while discrimination was noted in workplaces (41.7%), by family/friends (45.2%), cohabitants (15.7%), and healthcare (34.8%). Stigma significantly impacted healthcare-seeking behavior, with 33% avoiding medical care due to fear of discrimination. Psychologically, 50.4% reported low mood/anxiety, and 72.7% of those frequently fearing Mpox also experienced these symptoms. The outbreak led 71.3% to alter sexual behavior, primarily reducing encounters (60%). Fear of Mpox was strongly associated with behavioral changes (p < 0.001).ConclusionThe Mpox outbreak has exacerbated stigma toward MSM, highlighting an urgent need for intervention. Authorities, media, and community leaders must disseminate accurate information and implement psychological support programs to mitigate stigma and its detrimental effects on MSM.

Marco Favier, Toon Calders

The pipeline of a fair ML practitioner is generally divided into three phases: 1) Selecting a fairness measure. 2) Choosing a model that minimizes this measure. 3) Maximizing the model's performance on the data. In the context of group fairness, this approach often obscures implicit assumptions about how bias is introduced into the data. For instance, in binary classification, it is often assumed that the best model, with equal fairness, is the one with better performance. However, this belief already imposes specific properties on the process that introduced bias. More precisely, we are already assuming that the biasing process is a monotonic function of the fair scores, dependent solely on the sensitive attribute. We formally prove this claim regarding several implicit fairness assumptions. This leads, in our view, to two possible conclusions: either the behavior of the biasing process is more complex than mere monotonicity, which means we need to identify and reject our implicit assumptions in order to develop models capable of tackling more complex situations; or the bias introduced in the data behaves predictably, implying that many of the developed models are superfluous.

Catherine O. Egbe, Portia Nevhungoni, Mukhethwa Londani

<b>Background/Objectives:</b> Tobacco smoking is a cause of premature death and illness globally. This study examined the prevalence and factors associated with tobacco smoking among South African adults according to sex, education, and employment status among socio-demographic subgroups. <b>Methods:</b> Data were obtained from the 2021 South African Global Adult Tobacco Survey (a nationally representative household survey using a multi-stage stratified cluster random sampling design). The 6311 participants were aged 15+ years. Descriptive statistics, chi-square tests, and multiple logistic regression analysis were used to investigate relationships between current smoking and socio-demographic variables. <b>Results:</b> Smoking prevalence was 25.8% (n = 1573) (41.2% among men, 11.5% among women; 29.9% among working individuals, 23.1% among non-working individuals). The multiple logistic regression results showed that males aged 45–64 were twice as likely to be currently smoking than males aged 15–24. Coloured (of mixed race) males were twice as likely to smoke as Black African males. Males with secondary/post-secondary education were less likely to smoke compared to males with no education. Coloured females were seven times and White females were almost five times more likely to smoke than Black African females. Females with post-secondary education were less likely to smoke than those with no formal education. Females earning an income were more likely to smoke compared to those with no income. Non-working participants aged 25–44 and 45–64 were more likely to smoke compared to those aged 15–24 years. Working and non-working Coloured and working White individuals were three times more likely to smoke than working or non-working Black Africans. <b>Conclusion:</b> Groups with higher identified smoking prevalence may indicate where smoking cessation interventions should be targeted to reduce national smoking prevalence.